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Illinois Statewide Travel Demand Model Technical Approach. Joshua Auld, Behzad Karimi, Zahra Pourabdollahi, Kouros Mohammadian October 10, 2014. Illinois Department of Transportation. Outline. Introduction Methodology Long distance travel Freight External/Rural Data Collection - PowerPoint PPT Presentation
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Joshua Auld, Behzad Karimi, Zahra Pourabdollahi, Kouros Mohammadian
October 10, 2014
Illinois Statewide Travel Demand ModelTechnical Approach
Illinois Department of Transportation
Outline
• Introduction• Methodology
• Long distance travel• Freight• External/Rural
• Data Collection• Network Development• Simulation System• Future Tasks
2
INTRODUCTION
3
Statewide Models
• Statewide models increasingly common as states struggle with complex transportation issues that must be studied from a system perspective
• Portion of travel not modeled directly in MPO models:• 34% of all person miles traveled from long-distance trips (NHTS 2009)• Freight trucks account for 10% of vehicle miles traveled (NTS 2011)
• Provide the opportunity to evaluate the entire system in an integrated framework • Results in better understanding of travel behavior across the state• Cover area beyond MPO borders - crucial for future land use development
• Help with better planning of transportation services for all modes
• More efficient infrastructure planning and management for the• Demand management, supply management, safety, economic development, land-use
4
Statewide Models
• Applied for projects ranging from local traffic to multi-state corridor studies. The range of applications include:1. Statewide Plan: as a common ingredient to many components of a
plan including transportation systems analysis, scenario analysis, economic benefits and environmental analysis.
2. Local Planning: The model could be used for local planning in the smaller rural communities.
3. Long Distance Corridors: The integration of long distance travel and freight movements can make the comparison of alternatives in a corridor possible.
4. Support for local and regional models: the statewide model can be used to find external trips, truck trips and other modes to be used in the smaller model.
• Our study will focus on items 3 and 4.
5
UIC’s Project
• MPOs across the state have developed sophisticated transportation demand models that are used to determine travel demand• Such models could greatly benefit from more complete long-distance
personal travel and truck freight movement data.
• Researchers at University of Illinois at Chicago (UIC) examine statewide long-distance travel and truck freight movement within the state and parts of the larger region that directly impact our state.
• “Long Distance Travel” is defined similar to the National Household Travel Survey as those trips that are at least 100 minutes (approximately 75 miles or more).
• MPOs can use the model to modify their existing TDM to more accurately depict travel and freight behavior, thus having better information and more efficiently plan or manage transportation system.
6
Current Status of Statewide Modeling
• Current status of statewide models (Alan Horowitz)
7
Current Status of Statewide Modeling
• Technical approaches used in statewide:• Traffic count and growth factor (e.g. Montana)• Four-step model• Activity- and Tour-based microsimulation model (e.g. Ohio and
Oregon)
• Cost to develop the model highly depends on the technical approach:• $25,000 for South Carolina to millions of dollars for states of
Ohio and Oregon• considerable portion of spent costs and time in tour- and activity-
based models goes to data collection efforts• It costs $3,500,000 for Ohio to collect needed data for the being
revised model
8
Current Status of Statewide Modeling
• Time to develop the model also depends on the technical approach:• 6 months for Traffic count and growth factor model• 8 years for integrated tour-based model
• Statewide models are moving toward a more detailed zone systems and networks. • The second generation of Oregon statewide model, called
SWIM2, has over 3,000 zones and over 53,000 links and it is while SWIM1 had only 125 zones and 2,000 links.
9
Model Development Through Collaboration
• Given the size / scope of this work collaboration necessary• Collaboration on model development:
• Argonne National Laboratory computing resources, network editing and simulation software
• UIC: freight modeling, activity-based modeling survey implementation
• MPOs: network and land-use information, demand model results
• Result of this work should be useful to many agencies• IDOT for long distance planning purposes• External and freight trips for local MPO models
10
Current Status of Statewide Modeling
• Market Segmentation is crucial to deal with heterogeneity:• Short- and Long-distance trips • Trip purposes
• Combination of trip purposes in short- and long-distance trips can be very different
• Freight and Passenger
11
Current Status of Statewide Modeling
• Threshold distance between long and short distance trips varies from 50 miles (e.g. Oregon) to 100 miles (e.g. California)
• Based on the following chart, 75 miles for Chicago and 50 miles for other part of Illinois was selected as the distance threshold
12
5000
10
20
30
40
50
60
70
80
90
100
Chicago MSA
IL_OtherMSAs
OR
OH
CA
Trip Distance (Mile)
Cu
mu
lati
ve D
isti
bu
tio
n o
f V
MT
s
Data Source: NHTS 2001
METHODOLOGY
A World-Class Education, A World-Class City
ILSTDM Development Methodology
• Four primary components:• Long-distance passenger travel model• Freight model• Local travel demand
• MPO results where available• Default activity-based model for other areas
• Visitor & pass-through trips
A World-Class Education, A World-Class City
ILSTDM Methodology
15
Population Synthesis
Trip Frequency(Generation)
Trip Distribution (Location Choice)
Mode Choice
1. Long-Distance Travel Model
2. Freight Model
Long-distance trips
Freight trips
3. MPO and External
OD Tables
Diurnal Curves
Transims Convert Trips routine
Local/Visitor trips
4. Other Local
Local trips
Population Synthesis
Activity Generation
Destination Choice
Mode Choice
5. Network Simulation
FirmSynthesis
Supplier Selection
Shipment Size
Mode Choice
LONG-DISTANCE TRAVEL MODELING
A World-Class Education, A World-Class City
Long-Distance Travel Modeling
• Simulation of trips over 50/75 miles• Important for statewide planning
• Accounts for a significant portion of trips on interstates and state highways
• All trips on intercity bus, rail and air
• Long distance travel simulated for all residents of Illinois and neighboring counties
• Estimated using econometric activity-based model covering all primary modes
A World-Class Education, A World-Class City
Long-Distance Travel Model Framework
• Primary inputs:• Census data (ACS and 2010 SF1• TAZ Land use data from MPOs• Congested Network skims• Person and intercept survey results
• Five inter-related models• Generation, Distribution, Mode choice
connected through logsums.• Conditional time-of-day choice• Population synthesis using PopSyn
program developed for CMAP
Census Land Use
Synthetic Population
Long Distance Trip Generation
Trip Distribution
Mode Choice
Ridership
HH Survey
Intercept Surveys
Long-distance Travel
Time of day choice
Trip Generation
• ZINB count regression models:• Gives annual work/non-work trips• Utilizes logsums from destination
choice models• Estimated using weighted person
travel survey results
• Party size choice model• Ordered logit model for 1, 2, 3+
• Annual trip counts by household then used as input to daily trip realization model
Trip Generation Discussion• Factors associated with higher trip rates:
• Males, whites and high-income (all)• More vehicles (all)• More children (non-work trips)• Employment accessibility (work)• Destination log-sum (non-work)
• Decreased trip rates:• Larger households (work )• Cultural accessibility (non-work)
• Factors associated with higher zero trips• Low income (work)
• Factors associated with lower zero trip probability:• Larger households and households with children (all)• College educated and male (work)• Employed and married individuals (non-work)
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
35.0%
40.0%
45.0%
50.0%
0 1 2 3 4 5 6 7 8 9 10
SIM-Vacation ATS-Vacation HHSurvey - Vacation
Other trips: AverageSimulated 3.06 ATS 2.29 Survey 3.08
Destination Choice Models
• Two-level destination choice model:• Region-choice utilizes TAZ choice logsum
• 20 regions (including external regions)• TAZ choice in region
• Sample of regional TAZs• Uses mode choice logsum for TAZ Nested Logit Destination Choice
Region Choice
Chi Stl . . . External
TAZ1 TAZ2 TAZ N
LogsumLogsumLogsum
HH Survey
Intercept Surveys
Destination Choice TAZ results
Variable Coefficient t-stat p-value
Population 0.117 4.20 0
Employment 0.597 18.32 0Cultural Area 1.63 1.54 0.12 *
Major Recreational Area 1.21 3.40 0
Avg. Household Income -0.094 -4.45 0
Population Accessiblity -0.287 -2.02 0.04
Recreational Accessibility 0.024 1.68 0.09Retail Accessibility 0.103 1.49 0.14 *Total Employment Accessibility 0.177 6.93 0
University Accessibility 0.035 4.18 0
Mode Choice Logsum 0.951 4.00 0
Model Fit StatisticsNumber of observations: 828Likelihood ratio test: 866.6
Rho-square: 0.176
• Factors increasing TAZ utility:
• Increased population and employment
• Cultural and recreational opportunities
• Nearby employment
• Access to university and recreational areas
• Higher mode choice logsum
• Factors decreasing utility:
• Higher surrounding population
• Higher zonal average income
Mode Choice Models
• Two levels of MNL models:• Main mode choice – depends on access/egress logsums• Access / egress mode choice
• Estimated using weighted SP/RP survey data• Modal constants calibrated to observed survey distribution
ModeChoice
Auto Air BUS RAIL HSR
Access Mode
TransitAuto Taxi
Access LOGSUM
Egress Mode
TransitAuto Taxi
Egress LOGSUM
Intercept Surveys
ModeChoice
Auto Air BUS RAIL HSR
Access Mode
TransitAuto Taxi
Access LOGSUM
Egress Mode
TransitAuto Taxi
Egress LOGSUM
Intercept Surveys
Time of Day Choice Model
• Moving to daily travel model makes TOD component significant
• Estimate segmented time-of-day choice for each long distance trip
• Implemented using multinomial logit conditional on other choices
• Uses data collected from household travel survey
24
FREIGHT MODELING
25
Freight Model Methodology
26
FAME Framework
• Firm Synthesis
• Supply Chain Formation
• Logistics Decisions
• Shipments Forecasting
• Network Analysis
Firm SynthesisIntroducing individual decision-makers
Supplier SelectionDetermining trade relationships/supply chains
Shipment SizeUsing an iterative proportional fitting model
Mode ChoiceModal split between truck and rail
Freight Model Framework
Geographical Scale National Scale: Domestic freight flows
Zone System (333 zone) Township level zones in the Chicago area (118 zone) County level zones in rest of Illinois (95 zone) FAF zones in the rest of US (120 zone)
27
Freight Model Framework
Decision-making agents Firms : the decision-maker units
Producer/Receiver of goods Form supply chains Specify logistics choices
Firm-types : a group of firms with the same industry type employee size geographic location in the zoning system
28
Zone SystemZone System
Eco
no
mic
Act
ivit
yL
og
isti
cs C
ho
ices
Zoning System
Zoning System
Socio- EconomicFactors
Socio- EconomicFactors
Economic Activity Data
Economic Activity Data
Freight Generation Model
Commodity Production Consumption Rates
Supplier Selection Model
Network Assignment
Establishment Survey Establishment Survey
Annual Commodity Flow (firm-to-firm)
GPS data gatheringGPS data gathering
Supplier Evaluation Model
IO accounts/ Industry-commodity crosswalk
IO accounts/ Industry-commodity crosswalk
Establishment Freight Survey
Establishment Freight Survey
Transportation Performance Measures
Interview Survey (specialists)
Interview Survey (specialists)
CBP DataCBP Data
IO AccountsIO AccountsIndustry-
CommodityCrosswalk
Industry-CommodityCrosswalk
Firm Synthesis Model
List of Firms with Their Characteristics
Empty Trucks / Backhauling
Shipping Chain Configuration(direct/non-direct shipping chains)
Shipment Size / Frequency Choice Model
Main Mode Choice Model
Vehicle Choice Model
Number of Stops per Chain Model
Stop Type ModelAccess/Egress Mode
Choice Model
Simulated Individual Shipments
Net
wo
rk
An
alys
isNational Agent-Based Freight Model FrameworkFreight Modeling Framework
Economic Activity Overview
Zone SystemZone System
Eco
no
mic
Act
ivity Zoning
SystemZoningSystem
Socio- EconomicFactors
Socio- EconomicFactors
Economic Activity DataEconomic
Activity Data
Freight Generation Model
Commodity Production Consumption Rates
CBP DataCBP Data
IO AccountsIO Accounts
Industry-CommodityCrosswalk
Industry-CommodityCrosswalk
Firm Synthesis Model
List of Firms with Their
Characteristics
30
Firm Synthesis and Freight Generation
• Firm Synthesis:• 7,687,522 business establishments
Classified into 70,116 firm-type groups
31
Firm-Type: 130 236 1 (17)
Zone
NAICS
Employee Size
Number of EstablishmentsMenard County
Construction of Buildings
1-19 employee
Freight Generation Model Commodity-industry crosswalk Firm level production/consumption
rates Make-Use commodity-industry
crosswalks Number of establishments in zone Size of establishments (employee size)
Data Input-Output Accounts (BEA, 2013) Freight Analysis Framework (FAF) Commodity Flow Survey (CFS) Synthesized Firm-types
Logistics Choice Modeling OverviewLo
gist
ics
Cho
ices
Commodity Production Consumption Rates
Supplier Selection Model
Establishment Survey Establishment Survey
Annual Commodity Flow
(firm-to-firm)
GPS data gatheringGPS data gathering
Supplier Evaluation Model
IO accounts/ Industry-commodity crosswalk
IO accounts/ Industry-commodity crosswalk
Establishment Freight Survey
Establishment Freight Survey
Interview Survey (specialists)
Interview Survey (specialists)
List of Firms with Their
Characteristics
Shipping Chain Configuration(direct/non-direct shipping chains)
Shipment Size / Frequency Choice Model
Main Mode Choice Model
Vehicle Choice Model
Number of Stops per Chain Model
Stop Type Model
Access/Egress Mode Choice Model
Simulated Individual Shipments
32
Supplier Evaluation and Selection Model
• A two-step modeling framework
• Multi-criteria supplier evaluation model• To take into account decision makers’ opinions
• To calculate suitability score for each potential supplier
• Multi-criteria supplier selection optimization
model• Maximize total suitability score of selected suppliers
• Minimize total logistics costs
• Meet the production capacity of suppliers and cover total demand of buyers
33
Shipping Chain Configuration Model
• Shipping chain/Distribution channel/Transport chain• Modeling Approach
• Rule-based decision tree clustering method• Growth method: Exhaustive CHAID algorithm
• Number of intermediate stops in a chain & type of facility at each stop
34
Chemical manufacturing
Nonmetallic mineral product manufacturing218
3 KT Chemical and Pharmaceutical Products
One stop at a DistributionShipping Chain:
Center
Mode :TruckShipment size: 4K ~ 30K lbsActual weight: 29400 lbsAnnual frequency: 204
Network Analysis Framework
Network Assignment
Logistics Choice Models
Transportation Performance Measures
Empty Trucks / Backhauling
Simulated Individual Shipments
Ne
two
rk A
na
lysi
s
35
EXTERNAL TRIPS
36
External / MPO Trip Models
• 1995 American Travel Survey (ATS) used to compute base year long-distance trip distribution for U.S.
• Iterative proportional fitting procedure updates base trip distribution to 2010 using Census data
• Generate OD table for model zoning system
• Combined with MPO OD tables
• Converted to individual trips using Transims ConvertTrips utility + diurnal distribution assumptions
• External trips in Gravity Model formulation to include sensitivity to network changes
37
LOCAL TRAVEL
38
4. Local Travel Model: ADAPTS ABM
• Activity-based scheduling process model:• Bottom-up approach to activity-travel pattern formation• Activities generated, planned and scheduled dynamically• Planning process is explicitly modeled• Operationalized using multiple scheduling process surveys
• Integrated activity-travel microsimulation• Dynamic, multi-day activity-travel simulation• Activities planned, scheduled and executed in single framework• Fully agent-based: all aspects implemented as individual agent
behaviors – including routing and travel simulation
• Currently Implemented in POLARIS model framework• Estimated based on Chicago-region data – not adjusted to rural area
Local Travel Model Overview
40
Household ActivityGeneration
Individual ActivityGeneration
Each Planning Time-step(5-min intervals)
Modify plans
Schedule Departures
Destination Choice
Timing Choices
In continuous time
Mode Choices
Party Choice
Get Route
Check Activity Schedule
Planning order model
Simulation
Activity Scheduling
GenerationModel
Preprocessing
Gather Pre-trip info
Read Data and Scenario
Population Synthesis
Routine and PreplannedActivity Scheduling
DATA COLLECTION
41
Household Survey
• Collects trip data for long-distance trips at household level
• Similar to American Travel Survey 1995 – conducted as part of NHTS
• Collects:• Trip frequency• Travel modes• Trip type• Party composition
• Used to estimate trip frequency models• Dependent on household characteristics• Destination characteristics• Mode characteristics• Approximated logsums (accessibility-based)
• Can be combined statistically with NHTS and ATS to extend sample
42
Instrument Design
1. Introduction • Verify correct contact
information
2. Household roster and demographic information • Demographics for
respondent• Demographics for other
household members• Housing information
3. Trip screening questions• Number of trips in the past 12 months• Trip count by quarter, mode, purpose,
party size• Commuting trips
4. Trip detail for last trips (work and non-work)• Start date, duration• Origin, destination, station access• Travel Modes• Purpose
Household Survey
• Long distance trip purpose
44
• Long distance Mode Choice
Household Survey
• Departure Time-of-day choice
45
2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 220%
2%
4%
6%
8%
10%
12%
14%
16%
18%
Start time
Household Survey
• HH Income
46
HH Income
Less than $25,000
$25,000-$49,999
$50,000-$74,999
$75,000-$99,999
$100,000-$149,000
$150,000 or more
0%
5%
10%
15%
20%
25%
30%
11%
26%
22%
18%16%
6%
Freight Data Sources
47
• Publicly Available Data1. County Business Patterns
2. Industry input-output accounts
3. Commodity Flow Survey
4. Freight Analysis Framework
• Survey Data1. UIC establishment survey, 1st wave (2009)
2. UIC establishment survey, 2nd and 3rd waves (2010-2011)
UIC Establishment Survey (2010-2011)
• Data Collection Method
• telephone introductions
• e-mail blast campaigns
• web crawling
48
UIC Establishment Survey (2010-2011)
• Survey Design
• Participants: logistics or shipping managers of
firms
• Three major parts
• Characteristics of the business establishment
• Attributes of five most recent shipments
• Contact information
49
UIC Establishment Survey (2010-2011)
• Survey Results• Approximately 219,000 contacts nationwide• 657 establishment surveys • 970 useable shipment survey forms
1st wave 2nd wave
0 5 10 15 20 25 30 35 400
50
100
150
200
250
300
350
400
450
Days Since Survey Released
Num
ber
of P
arti
cipa
nts
0 10 20 30 400
20
40
60
80
100
120
140
160
180
200
Days Since Survey Released
Num
ber
of P
arti
cipa
nts
50
Data Acquisition from MPOs
MPO Network Zoning Land Use TDM ResultsChicago ü ü ü üSpringfield ü ü üChampaign ü ü üSt. Louis ü ü ü üBloomington ü ü ü üPeoria ü ü üQuad-Cities ü ü üDanville ü üDecaturKankakeeDekalbRockford
Dubuque
51
NETWORK DEVELOPMENT
52
ILSTDM Network Development
• Approximately 90,000 links
• Data sources:• MPO models• FAF2 Network• Illinois HPMS (IRIS)• Argonne Chicago network
53
Combining networks
• Widely varying networks depending on source:• 1-way vs 2-way links• Missing capacity, lanes, speeds• Disconnects (especially in HPMS)• Very little traffic control information
54
• Develop custom Python scripts to combine networks• Generate estimates for speed, capacity, etc. when not provided• Import all networks into a common database format• Sqlite open-source DBMS with Spatialite extensions• Compatible with Argonne Network Editing software
Network Editor
55
Adding a missing link
Correcting connectivity
Synthesized Intersection Controls
• Generate signal/stop information using Transims IntControl program• Estimates signal/sign warrants given link connectivity, link capacities,
link type and area type• Different outcomes by area type (Chicago, St.Louis, Other urban or
rural) and primary/secondary street• Timing/phasing are then estimated using the warrants and a given
signal type, cycle length, etc.
56
ILSTDM Zone System Development
57
Region (Superzone) OrganizationTAZs Census TractsChicago Rest of Northern IllinoisChicago Suburbs Rest of Central IllinoisSt. Louis Rest of Southern IllinoisSt. Louis Suburbs CountiesChampaign Iowa - NeighboringSpringfield Wisconsin - NeighboringQuad Cities Indiana - NeighboringPeoria Missouri - NeighboringBloomington Kentucky - NeighboringDecatur StatesDanville Northeast (states)
Northwest (states)Southeast (states)Southwest (states)
• 5800 zones in 20 regions
Socio-Economic and Land Use Data Collection
• Many areas lack required zone data• Process to generate employment and land use:
• Extract employment by Zip Code from Zip Code Business Patterns• Plot Illinois Land coverage for ILGS, showing developed areas• Transfer zip code employment to developed areas through overlay
(assumes equal distribution of jobs throughout the developed area in each zip code• Transfer employment from developed
area shapes to zones through overlay• Aggregate to county level and perform
IPF to match county totals• Follow same process for Census of
Governments data• Land use overlay from ESRI points of
interest/landmarks dataset
58
SIMULATION SYSTEM
59
Simulation-BasedDynamic Traffic Assignment Model
• Trips enter traffic simulation either:• Directly: Long-distance model, Freight model, ADAPTS• Indirectly from OD tables converted to trips by Transims
• Network simulation contains:• Route Choice Model• En-route Switching Model• Traffic Control Model• Mesoscopic Traffic Simulation Model
• Currently implemented in POLARIS agent-based simulation framework
60
Route choice model
• One-Shot Assignment using prevailing travel information• Averaged experienced travel times in last interval (e.g. 5 minutes)• Travel times are output from traffic simulation model• Current implemented route choice model is pre-trip route choice model
with enroute replanning• Pre-trip route choice model is for pre-trip users who use the travel time
information based on current traffic conditions to find a shortest path from his/her origin to destination. (e.g. using google map to compute shortest path considering traffic at that time)
• Shortest path algorithm: individual link-based A-Star Algorithm that takes care of delay at turn movement
• Enroute replanning: travelers can switch routes at decision points based on experienced travel times using bounded rationality model
• This solution maintains an approximation of an instantaneous equilibrium
Traffic Simulation Model
• Newell’s Simplified Kinematic Wave model• Using cumulative curves• Capturing queue formation, spillback, and dispersion• Capturing shock wave• Adhering to the fundamental diagrams
• Output:• Network flow pattern
• Cumulative vehicles at upstream and downstream of a link• Vehicle trajectory (enter time and exit time of each link)
• Network performance• Time-dependent link travel time by turn movement
POLARIS Simulation Environment
63
Simulation Model Process
64
Population Synthesis
Trip Frequency(Generation)
Trip Distribution (Location Choice)
Mode Choice
Long-distance trips
Freight trips
OD Tables
Diurnal Curves
Transims Convert Trips routine
Local/Visitor trips Local trips
Population Synthesis
Activity Generation
Destination Choice
Mode Choice
Network Simulation
FirmSynthesis
Supplier Selection
Shipment Size
Mode Choice
Network Skims
Check Convergence
Results
NEXT STEPS
A World-Class Education, A World-Class City
Next Steps
• Estimate models using statewide data• Trip Generation, destination choice with new regions/zones, mode choice• Develop and estimate Time-of-day Models / diurnal distributions
• Implement models in POLARIS simulation framework• Local travel ABM already implemented• Integrate long-distance travel, freight travel models
• Model calibration and validation• Run model in calibration iteration scheme• Match to ground counts, survey observations where possible
• Policy Scenario Analysis• Work with stakeholders to implement scenarios of interest for testing
66
67
THANK YOU!
Illinois Department of Transportation